


import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
       from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import MobileNetVaImageProcessor



class        A     (		snake_case_	):







     '''simple docstring'''







     def 					UpperCAmelCase__       (				self    :							List[str]):
      _lowercase:       Tuple						=				self.config_class(**self.inputs_dict)
      self.parent.assertTrue(hasattr(_UpperCamelCase			,				"tf_padding"))
      self.parent.assertTrue(hasattr(_UpperCamelCase			,				"depth_multiplier"))






class        A     :







     '''simple docstring'''







     def __init__(				self    :							int			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							Dict=13			,				_UpperCamelCase    :							str=3			,				_UpperCamelCase    :							Optional[Any]=32			,				_UpperCamelCase    :							List[str]=0.2_5			,				_UpperCamelCase    :							Optional[Any]=8			,				_UpperCamelCase    :							Optional[Any]=8			,				_UpperCamelCase    :							Any=6			,				_UpperCamelCase    :							str=32			,				_UpperCamelCase    :							Optional[Any]=True			,				_UpperCamelCase    :							Union[str, Any]=True			,				_UpperCamelCase    :							Any=True			,				_UpperCamelCase    :							Tuple="relu6"			,				_UpperCamelCase    :							Optional[int]=1_280			,				_UpperCamelCase    :							Union[str, Any]=0.1			,				_UpperCamelCase    :							Optional[Any]=0.0_2			,				_UpperCamelCase    :							Optional[Any]=True			,				_UpperCamelCase    :							Tuple=True			,				_UpperCamelCase    :							int=10			,				_UpperCamelCase    :							List[str]=None			,				):
      _lowercase:       Union[str, Any]						=				parent
      _lowercase:       Dict						=				batch_size
      _lowercase:       Union[str, Any]						=				num_channels
      _lowercase:       Any						=				image_size
      _lowercase:       Union[str, Any]						=				depth_multiplier
      _lowercase:       Optional[Any]						=				depth_divisible_by
      _lowercase:       Union[str, Any]						=				min_depth
      _lowercase:       Any						=				expand_ratio
      _lowercase:       str						=				tf_padding
      _lowercase:       str						=				output_stride
      _lowercase:       Optional[int]						=				first_layer_is_expansion
      _lowercase:       Any						=				finegrained_output
      _lowercase:       Union[str, Any]						=				hidden_act
      _lowercase:       Dict						=				last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier)
      _lowercase:       Dict						=				classifier_dropout_prob
      _lowercase:       str						=				use_labels
      _lowercase:       Union[str, Any]						=				is_training
      _lowercase:       Dict						=				num_labels
      _lowercase:       int						=				initializer_range
      _lowercase:       Any						=				scope

     def 					UpperCAmelCase__       (				self    :							str):
      _lowercase:       Tuple						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      _lowercase:       Dict						=				None
      _lowercase:       List[str]						=				None
      if self.use_labels:
       _lowercase:       Union[str, Any]						=				ids_tensor([self.batch_size]			,				self.num_labels)
       _lowercase:       int						=				ids_tensor([self.batch_size, self.image_size, self.image_size]			,				self.num_labels)

      _lowercase:       Optional[Any]						=				self.get_config()

      return config, pixel_values, labels, pixel_labels

     def 					UpperCAmelCase__       (				self    :							Optional[Any]):
      return MobileNetVaConfig(
          num_channels=self.num_channels			,				image_size=self.image_size			,				depth_multiplier=self.depth_multiplier			,				depth_divisible_by=self.depth_divisible_by			,				min_depth=self.min_depth			,				expand_ratio=self.expand_ratio			,				output_stride=self.output_stride			,				first_layer_is_expansion=self.first_layer_is_expansion			,				finegrained_output=self.finegrained_output			,				hidden_act=self.hidden_act			,				tf_padding=self.tf_padding			,				classifier_dropout_prob=self.classifier_dropout_prob			,				initializer_range=self.initializer_range			,				)

     def 					UpperCAmelCase__       (				self    :							Any			,				_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							Optional[Any]):
      _lowercase:       List[Any]						=				MobileNetVaModel(config=_UpperCamelCase)
      model.to(_UpperCamelCase)
      model.eval()
      _lowercase:       Any						=				model(_UpperCamelCase)
      self.parent.assertEqual(
          result.last_hidden_state.shape			,				(
              self.batch_size,
              self.last_hidden_size,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )			,				)
      self.parent.assertEqual(
          result.pooler_output.shape			,				(self.batch_size, self.last_hidden_size)			,				)

     def 					UpperCAmelCase__       (				self    :							Tuple			,				_UpperCamelCase    :							List[Any]			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							List[Any]			,				_UpperCamelCase    :							Any):
      _lowercase:       List[str]						=				self.num_labels
      _lowercase:       Optional[int]						=				MobileNetVaForImageClassification(_UpperCamelCase)
      model.to(_UpperCamelCase)
      model.eval()
      _lowercase:       List[str]						=				model(_UpperCamelCase			,				labels=_UpperCamelCase)
      self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels))

     def 					UpperCAmelCase__       (				self    :							List[str]			,				_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							List[str]			,				_UpperCamelCase    :							str):
      _lowercase:       List[str]						=				self.num_labels
      _lowercase:       Union[str, Any]						=				MobileNetVaForSemanticSegmentation(_UpperCamelCase)
      model.to(_UpperCamelCase)
      model.eval()
      _lowercase:       Any						=				model(_UpperCamelCase)
      self.parent.assertEqual(
          result.logits.shape			,				(
              self.batch_size,
              self.num_labels,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )			,				)
      _lowercase:       Any						=				model(_UpperCamelCase			,				labels=_UpperCamelCase)
      self.parent.assertEqual(
          result.logits.shape			,				(
              self.batch_size,
              self.num_labels,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )			,				)






     def 					UpperCAmelCase__       (				self    :							Optional[int]):
      _lowercase:       List[Any]						=				self.prepare_config_and_inputs()
      _lowercase					,	_lowercase					,	_lowercase					,	_lowercase:       int						=				config_and_inputs
      _lowercase:       Any						=				{"pixel_values": pixel_values}
      return config, inputs_dict






@require_torch
class        A     (		snake_case_	, snake_case_	, unittest.TestCase	):







     '''simple docstring'''







     lowerCamelCase :						List[str]            =   (
         (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     lowerCamelCase :						Tuple            =   (
         {
             """feature-extraction""": MobileNetVaModel,
             """image-classification""": MobileNetVaForImageClassification,
             """image-segmentation""": MobileNetVaForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     lowerCamelCase :						Any            =   False
     lowerCamelCase :						List[Any]            =   False
     lowerCamelCase :						List[str]            =   False
     lowerCamelCase :						Optional[int]            =   False

     def 					UpperCAmelCase__       (				self    :							int):
      _lowercase:       Union[str, Any]						=				MobileNetVaModelTester(self)
      _lowercase:       Tuple						=				MobileNetVaConfigTester(self			,				config_class=_UpperCamelCase			,				has_text_modality=_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							Any):
      self.config_tester.run_common_tests()

     @unittest.skip(reason="MobileNetV2 does not use inputs_embeds")
     def 					UpperCAmelCase__       (				self    :							Any):
      pass

     @unittest.skip(reason="MobileNetV2 does not support input and output embeddings")
     def 					UpperCAmelCase__       (				self    :							Optional[int]):
      pass

     @unittest.skip(reason="MobileNetV2 does not output attentions")
     def 					UpperCAmelCase__       (				self    :							Any):
      pass

     def 					UpperCAmelCase__       (				self    :							str):
      _lowercase					,	_lowercase:       Optional[int]						=				self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       _lowercase:       Tuple						=				model_class(_UpperCamelCase)
       _lowercase:       List[Any]						=				inspect.signature(model.forward)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       _lowercase:       Optional[Any]						=				[*signature.parameters.keys()]

       _lowercase:       str						=				["pixel_values"]
       self.assertListEqual(arg_names[:1]			,				_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							Any):
      _lowercase:       int						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							List[Any]):
      def check_hidden_states_output(_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							List[Any]):
       _lowercase:       Optional[Any]						=				model_class(_UpperCamelCase)
       model.to(_UpperCamelCase)
       model.eval()

       with torch.no_grad():
        _lowercase:       List[Any]						=				model(**self._prepare_for_class(_UpperCamelCase			,				_UpperCamelCase))

       _lowercase:       Optional[int]						=				outputs.hidden_states

       _lowercase:       str						=				16
       self.assertEqual(len(_UpperCamelCase)			,				_UpperCamelCase)

      _lowercase					,	_lowercase:       Optional[int]						=				self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       _lowercase:       Tuple						=				True
       check_hidden_states_output(_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _lowercase:       List[Any]						=				True

       check_hidden_states_output(_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							Tuple):
      _lowercase:       str						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							int):
      _lowercase:       Tuple						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase)






     @slow
     def 					UpperCAmelCase__       (				self    :							Any):
      for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase:       int						=				MobileNetVaModel.from_pretrained(_UpperCamelCase)
       self.assertIsNotNone(_UpperCamelCase)






def 							__lowerCAmelCase      (			):
 _lowercase:       str						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
 return image





@require_torch
@require_vision
class        A     (		unittest.TestCase	):







     '''simple docstring'''







     @cached_property
     def 					UpperCAmelCase__       (				self    :							Tuple):
      return (
          MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224") if is_vision_available() else None
      )

     @slow
     def 					UpperCAmelCase__       (				self    :							Optional[Any]):
      _lowercase:       int						=				MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224").to(_UpperCamelCase)

      _lowercase:       Union[str, Any]						=				self.default_image_processor
      _lowercase:       Optional[int]						=				prepare_img()
      _lowercase:       List[Any]						=				image_processor(images=_UpperCamelCase			,				return_tensors="pt").to(_UpperCamelCase)

      # forward pass
      with torch.no_grad():
       _lowercase:       Optional[Any]						=				model(**_UpperCamelCase)

      # verify the logits
      _lowercase:       Tuple						=				torch.Size((1, 1_001))
      self.assertEqual(outputs.logits.shape			,				_UpperCamelCase)

      _lowercase:       Any						=				torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]).to(_UpperCamelCase)

      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,				_UpperCamelCase			,				atol=1e-4))






     @slow
     def 					UpperCAmelCase__       (				self    :							Optional[int]):
      _lowercase:       str						=				MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
      _lowercase:       Optional[Any]						=				model.to(_UpperCamelCase)

      _lowercase:       int						=				MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")

      _lowercase:       List[str]						=				prepare_img()
      _lowercase:       Dict						=				image_processor(images=_UpperCamelCase			,				return_tensors="pt").to(_UpperCamelCase)

      # forward pass
      with torch.no_grad():
       _lowercase:       List[str]						=				model(**_UpperCamelCase)
      _lowercase:       int						=				outputs.logits

      # verify the logits
      _lowercase:       Optional[int]						=				torch.Size((1, 21, 65, 65))
      self.assertEqual(logits.shape			,				_UpperCamelCase)

      _lowercase:       Dict						=				torch.tensor(
          [
              [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
              [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
              [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
          ]			,				device=_UpperCamelCase			,				)

      self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,				_UpperCamelCase			,				atol=1e-4))


"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
